Computer Science

Computer Science ›› 2025, Vol. 52 ›› Issue (11A): 241200033-9.doi: 10.11896/jsjkx.241200033

• Computer Software & Architecture • Previous Articles     Next Articles

Multi-agent Collaborative Code Generation Technology Driven by Large Language Models

XIA Peng, ZHANG Yijun, QI Ji   

  1. China Mobile(Suzhou) Software Technology Co.,Ltd.,Suzhou,Jiangsu 215000,China
  • Online:2025-11-15 Published:2025-11-10
  • About author:XIA Peng,born in 1988,Ph.D,senior engineer,is a member of CCF(No.U6207M).His main research interests include multimodal large models and multi-agent collaboration driven by large models.
    QI Ji,born in 1978,Ph.D,senior engineer.His main research interests include pre-trained large models,big data,and cloud computing.
  • Supported by:
    National Key Research and Development Program of China(2021YFB2801800).

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Abstract: In code generation tasks,pretrained large language models and agents have become key technologies for improving the quality and efficiency of code generation.However,when facing complex programming problems,intelligent agents based on large language models still struggle to provide effective solutions.This paper proposes a framework of multi-agent collaborative code generation to solve complex programming problems through the collaboration among agents,which includes four stages:problem analysis,task planning,code generation,and code debugging.The different base model strategies for agents based on open-source LLMs are proposed and the impact on system performance is tested.Additionally,an iterative programming paradigm incorporating reflection and debugging loops is introduced to optimize code generation based on feedback from each stage.Experimental results demonstrate that the multi-agent collaborative approach achieves significant performance improvements compared to traditional direct code generation methods across multiple datasets.Particularly,the hybrid model strategy achieves optimal perfor-mance on all tested datasets.Performance on test datasets is further improved with the adoption of reflection and debugging loops.

Key words: Multi-agent system, Large language model, Natural language processing, Code generation, Chain of thought

CLC Number: 

  • TP391
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